A Machine Learning Approach with Support Vector Machine (SVM) for Prediction of Preoperative Lymph Node Status with MR Images and clinical features for Intrahepatic Cholangiocarcinoma

Xu L.; Yang P.; Liang W.; Xing L.; Niu T.; Huang M.

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A Machine Learning Approach with Support Vector Machine (SVM) for Prediction of Preoperative Lymph Node Status with MR Images and clinical features for Intrahepatic Cholangiocarcinoma

机译：具有支持向量机（SVM）的机器学习方法，用于预测术前淋巴结状态与肝内胆管癌的MR图像和临床特征

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来源
《International Journal of Radiation Oncology, Biology, Physics》 |2019年第1suppla期|共1页
作者
Xu L.; Yang P.; Liang W.; Xing L.; Niu T.; Huang M.;
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作者单位

Zhejiang Univ Inst Translat Med Hangzhou Zhejiang Peoples R China;

Zhejiang Univ Inst Translat Med Hangzhou Zhejiang Peoples R China;

Zhejiang Univ Sch Med Affiliated Hosp 1 Dept Radiol Hangzhou Zhejiang Peoples R China;

Stanford Univ Stanford CA 94305 USA;

Zhejiang Univ Inst Translat Med Hangzhou Zhejiang Peoples R China;

Duke Univ Sch Med Radiat Oncol Durham NC USA;

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原文格式 PDF
正文语种 eng
中图分类放射医学;
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入库时间 2022-08-19 17:11:54

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1. A Machine Learning Approach with Support Vector Machine (SVM) for Prediction of Preoperative Lymph Node Status with MR Images and clinical features for Intrahepatic Cholangiocarcinoma [J] . Xu L., Yang P., Liang W., International Journal of Radiation Oncology, Biology, Physics . 2019,第1Suppla期

机译：具有支持向量机（SVM）的机器学习方法，用于预测术前淋巴结状态与肝内胆管癌的MR图像和临床特征
2. Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning [J] . Rostami Omid, Kaveh Mehrdad Computational Geosciences . 2021,第3期

机译：基于生物地理的优化（BBO），人造蜜蜂（ABC）和支持向量机（SVM）的SAR图像分类的最佳特征选择：优化和机器学习的组合方法
3. Correction: A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model [J] . Xiaopeng Yao, Xinqiao Huang, Chunmei Yang, JMIR Medical Informatics . 2021,第1期

机译：校正：评估肝胆管癌分化度和淋巴结转移的新方法：使用基于辐射族粒子群优化的预测和支持向量机模型
4. Gastric Lymph Node Cancer Detection Using Multiple Features Support Vector Machine for Pathology Diagnosis Support System [C] . T. Ishikawa, J. Takahashi, H. Takemura International Conference on Biomedical Engineering . 2014

机译：使用多个功能胃淋巴结癌症检测支持矢量机用于病理诊断支持系统
5. Combining biomarkers and clinicopathologic factors for prediction of response to adjuvant chemotherapy for breast cancer: Cox model and Support Vector Machine (SVM) methods. [D] . Liu, Xudong. 2010

机译：结合生物标志物和临床病理因素预测乳腺癌对辅助化疗的反应：Cox模型和支持向量机（SVM）方法。
6. A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma [O] . Lei Xu, Pengfei Yang, Wenjie Liang, 2019

机译：基于支持向量机的放射学方法基于MR图像的肝内胆管癌术前淋巴结状态评估
7. A Machine Learning Approach with Support Vector Machine (SVM) for Prediction of Preoperative Lymph Node Status with MR Images and clinical features for Intrahepatic Cholangiocarcinoma [O] . L. Xu, P. Yang, W. Liang, 2019

机译：具有支持向量机（SVM）的机器学习方法，用于预测术前淋巴结状态与肝内胆管癌的MR图像和临床特征

A Machine Learning Approach with Support Vector Machine (SVM) for Prediction of Preoperative Lymph Node Status with MR Images and clinical features for Intrahepatic Cholangiocarcinoma

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